BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model designed to understand the context of words in a sentence by processing them bidirectionally. Unlike traditional language models, which read text sequentially (left-to-right or right-to-left), BERT considers both directions simultaneously. This enables it to capture nuanced relationships and context.
BERT is pre-trained on large datasets using two tasks: masked language modeling (predicting masked words in a sentence) and next sentence prediction (understanding sentence relationships). These tasks help it generalize well across various NLP applications.
Its popularity stems from its state-of-the-art performance in tasks like question answering, sentiment analysis, and named entity recognition. BERT has become the foundation for numerous NLP models, with variations like RoBERTa and DistilBERT offering improvements in speed and efficiency. Hugging Face Transformers and TensorFlow provide easy access to pre-trained BERT models, making it accessible to developers for fine-tuning on specific tasks.